• Journal of Industrial Technology
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• v.24 no.A
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• pp.99-107
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• 2004

This paper considers reinforcement learning control with the self-organizing map. Reinforcement learning uses the observable states of objective system and signals from interaction of the system and the environments as input data. For fast learning in neural network training, it is necessary to reduce learning data. In this paper, we use the self-organizing map to parition the observable states. Partitioning states reduces the number of learning data which is used for training neural networks. And neural dynamic programming design method is used for the controller. For evaluating the designed reinforcement learning controller, an inverted pendulum of the cart system is simulated. The designed controller is composed of serial connection of self-organizing map and two Multi-layer Feed-Forward Neural Networks.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.77-85
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• 2008

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.03b
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• pp.84-87
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• 1999

This paper presents a preform design method that combines the analytic method and inference of known knowledge with neural network. The analytic method is a finite element method that is used to simulate backward extrusion with pre-defined process parameters. The multi-layer network and back-propagation algorithm are utilized to learn the training examples from the simulation results. The design procedures are utilized to learn the training examples from the simulation results. The design procedures are two methods the first the neural network infer the deformed shape from the pre-defined processes parameters. The other the network infer the processes parameters from deformed shape. Especially the latest method is very useful to design the preform From the desired feature it is possible to determine the processes parameters such as friction stroke and tooling geometry. The proposed method is useful for shop floor to decide the processes parameters and preform shapes for producing sound product.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.364-370
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• 2005

LBL(Layer-By-Layer) algorithms have been proposed to accelerate the training speed of MLPs(Multilayer Perceptrons). In this LBL algorithms, each layer needs a error function for optimization. Especially, error function for hidden layer has a great effect to achieve good performance. In this sense, this paper proposes a new hidden layer error function for improving the performance of LBL algorithm for MLPs. The hidden layer error function is derived from the mean squared error of output layer. Effectiveness of the proposed error function was demonstrated for a handwritten digit recognition and an isolated-word recognition tasks and very fast learning convergence was obtained.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.666-671
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• 2010

Multi-layer perceptron neural networks have been constructed to classify four types of defects in steam generator tubes. Three features are extracted from the signals of the eddy current testing method. These include maximum impedance, phase angle at the point of maximum impedance, and an angle between the point of maximum impedance and the point of half the maximum impedance. Two hundred sets of these features are used for training and assessing the networks. Two approaches are involved to train the networks and to classify the defect type. One is the conjugate gradient method and the other is the Broydon-Fletcher-Goldfarb-Shanno method which is recognized as the most popular algorithm of quasi-Newton methods. It is found from the computation results that the training time of the Broydon-Fletcher-Goldfarb-Shanno method is much faster than that of the conjugate gradient method in most cases. On the other hand, no significant difference of the classification performance between the two methods is observed.

• 한국항공운항학회:학술대회논문집
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• 2015.11a
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• pp.282-285
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• 2015

최근 항공산업은 항공 기술과 운항환경의 발달로 상당히 안전화되었으나, 인적요인(Human Factor)에 의한 사고 및 준 사고는 계속 발생하고 있다. 따라서 조종사의 훈련은 항공안전을 위한 가장 중요한 과제이나 각국의 규정에서 정하는 훈련과 실제 필요한 훈련 사이에 간격이 있어, 이를 향상하기 위해서 역량기반훈련을 기반으로 한 부조종사 자격제도(Multi-Crew Pilot License, MPL), 증거기반훈련(Evidence Based Training, EBT), 향상된 자격프로그램(Advanced Qualification Programme, AQP)이 개발되었다. 항공 선진국의 항공사들은 기존의 법정 요구량 충족위주 훈련과 항목 중심의 획일적인(One size fits all) 훈련의 패러다임을 개선하여 국제민간항공기구(International Civil Aviation Organization, ICAO)와 국제항공운송협회(International Air Transport Association, IATA)에서 제시하는 데로 개인의 역량을 더 향상 시킬 수 있는 효율적인 훈련 프로그램을 실시하여 사고 유발 인적요인을 감소시키고 있다. 역량을 중심으로 하는 조종사 훈련은 변화하는 복잡한 환경에서 예상하지 못한 위협이 발생하였을 때, 조종사들이 적절히 대처 할 수 있는 레질리언트 크루(Resilient Crew)를 양성하는 데 효과적이다.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.31-37
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• 2017

There are lots of combined battlefield elements which complete the war. It looks problematic when collecting and analyzing these elements and then predicting the situation of war. Commander's experience and military power assessment have widely been used to come up with these problems, then simulated combat training program recently supplements the war-game models through recording real-time simulated combat data. Nevertheless, there are challenges to assess winning factors of combat. In this paper, we characterize the combat element (ce) by clustering simulated combat data, and then suggest multi-layered artificial neural network (ANN) model, which can comprehend non-linear, cross-connected effects among ces to assess mission completion degree (MCD). Through our ANN model, we have the chance of analyzing and predicting winning factors. Experimental results show that our ANN model can explain MCDs through networking ces which overperform multiple linear regression model. Moreover, sensitivity analysis of ces will be the basis of predicting combat situation.

• Structural Engineering and Mechanics
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• v.49 no.6
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• pp.687-703
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• 2014

This paper develops a new nonlinear model for active control of three-dimensional (3D) irregular building structures. Both geometrical and material nonlinearities with a neuro-controller training algorithm are applied to a multi-degree-of-freedom 3D system. Two dynamic assembling motions are considered simultaneously in the control model such as coupling between torsional and lateral responses of the structure and interaction between the structural system and the actuators. The proposed control system and training algorithm of the structural system are evaluated by simulating the responses of the structure under the El-Centro 1940 earthquake excitation. In the numerical example, the 3D three-story structure with linear and nonlinear stiffness is controlled by a trained neural network. The actuator dynamics, control time delay and incident angle of earthquake are also considered in the simulation. Results show that the proposed control algorithm for 3D buildings is effective in structural control.

• Speech Sciences
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• v.11 no.4
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• pp.129-141
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• 2004

An auditory-perceptual evaluation has long been utilized in assessing dysarthric speech. The process involves subjective judgement and the results might vary depending on clinical experiences or training of listeners. This study aimed to investigate reliability of the auditory-perceptual evaluation of 22 multi -dimensional variables on 6 patients with Parkinsonian speech disorders. Listeners were divided into two groups: one consisted of 6 speech therapists with clinical experiences for three years or more, and the other 6 graduate students without any previous clinical background. The results showed that the former evaluated dysarthric speech with higher inter-rater and intra-rater reliabilities than the latter. Furthermore, such speech variables as 'precise consonant: 'speech intelligibility: and 'SMR regularity' were more influenced than others by clinical experiences. We, therefore, postulated that a reliable auditory-perceptual evaluation of dysarthric speech may require adequate amount of clinical training of listeners.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.353-360
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• 2004

This paper considers reinforcement learning control with the self-organizing map. Reinforcement learning uses the observable states of objective system and signals from interaction of the system and environment as input data. For fast learning in neural network training, it is necessary to reduce learning data. In this paper, we use the self-organizing map to partition the observable states. Partitioning states reduces the number of learning data which is used for training neural networks. And neural dynamic programming design method is used for the controller. For evaluating the designed reinforcement learning controller, an inverted pendulum on the cart system is simulated. The designed controller is composed of serial connection of self-organizing map and two Multi-layer Feed-Forward Neural Networks.